import numpy as np
from scipy.optimize import minimize

# 步骤1：声音能量计算
def calculate_sound_energy(sample_points):
    energy = np.sum(sample_points ** 2)
    return energy

# 步骤2：时延信息获取
def calculate_delay(sensor_position, sound_speed):
    distance = np.linalg.norm(sensor_position)
    delay = distance / sound_speed
    return delay

# 步骤3：波束成形
def beamforming(sensor_weights, sound_signal):
    beamformed_signal = np.dot(sensor_weights, sound_signal)
    return beamformed_signal

# 步骤4：球型插值定位法
def spherical_interpolation(sensor_positions, sensor_delays, sensor_energies, sound_speed, source_position):
    # 步骤3：使用网格搜索来寻找最佳声源坐标
    def grid_search():
        r_range = np.linspace(0.1, 10.0, 100)
        theta_range = np.linspace(0, np.pi, 100)
        phi_range = np.linspace(0, 2*np.pi, 100)

        best_coordinates = None
        best_residual = float('inf')

        for r in r_range:
            for theta in theta_range:
                for phi in phi_range:
                    initial_guess = [r, theta, phi]
                    residual = objective_function(initial_guess, source_position)

                    if residual < best_residual:
                        best_residual = residual
                        best_coordinates = initial_guess

        return best_coordinates

    # 步骤4：判断声源坐标是否在设备的三维区域内（需要根据实际情况实现）
    def is_source_in_device_region():
        # 这里需要根据最优坐标进行判断，确保声源坐标在设备的合理区域内
        if ...:
            return True
        else:
            return False

    # 使用网格搜索来寻找最佳声源坐标
    optimized_coordinates = grid_search()
    is_in_device_region = is_source_in_device_region()

    return optimized_coordinates, is_in_device_region

# 步骤5：定义目标函数，用于最小化
def objective_function(coordinates, source_position):
    r, theta, phi = coordinates  # 从坐标中解包 r、θ 和 ϕ
    x, y, z = source_position  # 声源坐标 (x, y, z)

    # 计算预测声源与传感器的相对时延信息
    relative_delays = [delay - min(sensor_delays) for delay in sensor_delays]

    # 计算预测声音能量
    predicted_energies = []
    for i in range(len(sensor_positions)):
        predicted_delay = r / sound_speed + (x * np.sin(theta) * np.cos(phi) +
                                             y * np.sin(theta) * np.sin(phi) +
                                             z * np.cos(theta)) / sound_speed

        # 添加条件来处理除以零或接近零的情况
        if predicted_delay < 1e-6:  # 设置一个较小的非零值，可以根据需要调整
            predicted_energy = 1.0  # 或者其他合适的值
        else:
            predicted_energy = 1 / (predicted_delay ** 2)  # 预测的声音能量

        predicted_energies.append(predicted_energy)

    # 计算残差平方和
    residual = np.sum([(predicted_energies[i] - sensor_energies[i]) ** 2 for i in range(len(sensor_positions))])
    return residual

# 示例数据
sample_points = np.array([0.1, 0.2, 0.3, 0.4, 0.5])  #这个NumPy数组包含表示声音信号的一系列样本点。
sensor_positions = np.array([[1, 2, 3], [2, 3, 4], [3, 4, 5]])  #NumPy数组表示三维空间中传感器的位置
sound_speed = 343
sensor_delays = [calculate_delay(sensor_position, sound_speed) for sensor_position in sensor_positions]
sensor_energies = [0.8, 0.9, 0.7]   #个列表包含与每个传感器的测量相关联的能量值。这些值似乎表示每个传感器接收到的声音信号的强度或强度

# 步骤1：声音能量计算
energy = calculate_sound_energy(sample_points)
threshold = 0.3

if energy > threshold:
    print("存在异常声音")
else:
    print("无异常声音")

# 步骤3：波束成形
sensor_weights = np.array([0.5, 0.6, 0.7])
sound_signal = np.array([0.3, 0.4, 0.5])
beamformed_signal = beamforming(sensor_weights, sound_signal)
print("波束成形后的输出信号")

# 定义声源坐标 (x, y, z)
#source_position = np.array([1.4, 2.5, 3.5])

# 获取声源的定位信息
optimized_coordinates, is_in_device_region = spherical_interpolation(sensor_positions, sensor_delays, sensor_energies, sound_speed, source_position)

# 输出声源的定位信息和声源坐标
if is_in_device_region:
    print("声源定位坐标 (r, θ, ϕ)：", optimized_coordinates)
    print("声源坐标 (x, y, z)：", source_position)
else:
    print("声源不在设备区域内")
